weather_df =
rnoaa:: meteo_pull_monitors(c("USW00094728", "USC00519397", "USS0023B17S"),
var = c("PRCP", "TMIN", "TMAX"),
date_min = "2017-01-01",
date_max = "2017-12-31") %>%
mutate(
name = recode(id, USW00094728 = "CentralPark_NY",
USC00519397 = "Waikiki_HA",
USS0023B17S = "Waterhole_WA"),
tmin = tmin / 10,
tmax = tmax / 10) %>%
select(name, id, everything())
## Registered S3 method overwritten by 'crul':
## method from
## as.character.form_file httr
## Registered S3 method overwritten by 'hoardr':
## method from
## print.cache_info httr
## file path: /Users/dionna_attinson/Library/Caches/rnoaa/ghcnd/USW00094728.dly
## file last updated: 2019-09-26 10:27:41
## file min/max dates: 1869-01-01 / 2019-09-30
## file path: /Users/dionna_attinson/Library/Caches/rnoaa/ghcnd/USC00519397.dly
## file last updated: 2019-09-26 10:27:57
## file min/max dates: 1965-01-01 / 2019-09-30
## file path: /Users/dionna_attinson/Library/Caches/rnoaa/ghcnd/USS0023B17S.dly
## file last updated: 2019-09-26 10:28:02
## file min/max dates: 1999-09-01 / 2019-09-30
weather_df
## # A tibble: 1,095 x 6
## name id date prcp tmax tmin
## <chr> <chr> <date> <dbl> <dbl> <dbl>
## 1 CentralPark_NY USW00094728 2017-01-01 0 8.9 4.4
## 2 CentralPark_NY USW00094728 2017-01-02 53 5 2.8
## 3 CentralPark_NY USW00094728 2017-01-03 147 6.1 3.9
## 4 CentralPark_NY USW00094728 2017-01-04 0 11.1 1.1
## 5 CentralPark_NY USW00094728 2017-01-05 0 1.1 -2.7
## 6 CentralPark_NY USW00094728 2017-01-06 13 0.6 -3.8
## 7 CentralPark_NY USW00094728 2017-01-07 81 -3.2 -6.6
## 8 CentralPark_NY USW00094728 2017-01-08 0 -3.8 -8.8
## 9 CentralPark_NY USW00094728 2017-01-09 0 -4.9 -9.9
## 10 CentralPark_NY USW00094728 2017-01-10 0 7.8 -6
## # … with 1,085 more rows
ggplot(weather_df, aes(x= tmin, y= tmax)) +
geom_point()
## Warning: Removed 15 rows containing missing values (geom_point).
alternate way of making this plot
weather_df %>%
ggplot(aes(x= tmin, y = tmax)) +
geom_point()
## Warning: Removed 15 rows containing missing values (geom_point).
saving initial plots
weather_df %>% filter(name == "CentralPark_NY")
## # A tibble: 365 x 6
## name id date prcp tmax tmin
## <chr> <chr> <date> <dbl> <dbl> <dbl>
## 1 CentralPark_NY USW00094728 2017-01-01 0 8.9 4.4
## 2 CentralPark_NY USW00094728 2017-01-02 53 5 2.8
## 3 CentralPark_NY USW00094728 2017-01-03 147 6.1 3.9
## 4 CentralPark_NY USW00094728 2017-01-04 0 11.1 1.1
## 5 CentralPark_NY USW00094728 2017-01-05 0 1.1 -2.7
## 6 CentralPark_NY USW00094728 2017-01-06 13 0.6 -3.8
## 7 CentralPark_NY USW00094728 2017-01-07 81 -3.2 -6.6
## 8 CentralPark_NY USW00094728 2017-01-08 0 -3.8 -8.8
## 9 CentralPark_NY USW00094728 2017-01-09 0 -4.9 -9.9
## 10 CentralPark_NY USW00094728 2017-01-10 0 7.8 -6
## # … with 355 more rows
scatterplot=
weather_df %>%
ggplot(aes(x= tmin, y= tmax)) +
geom_point()
scatterplot
## Warning: Removed 15 rows containing missing values (geom_point).
adding color…
weather_df %>%
ggplot(aes(x= tmin, y= tmax)) +
geom_point(aes(color=name), alpha = .4)
## Warning: Removed 15 rows containing missing values (geom_point).
vs
weather_df %>%
ggplot(aes(x = tmin, y= tmax, color = name)) +
geom_point(aes(color=name), alpha = .5) +
geom_smooth(se=FALSE)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 15 rows containing non-finite values (stat_smooth).
## Warning: Removed 15 rows containing missing values (geom_point).
facet!
ggplot(weather_df, aes(x = tmin, y = tmax, color = name)) +
geom_point(alpha = .5) +
geom_smooth(se = FALSE) +
facet_grid(. ~ name)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 15 rows containing non-finite values (stat_smooth).
## Warning: Removed 15 rows containing missing values (geom_point).
this is fine but not interesting
weather_df %>%
ggplot(aes(x = date, y = tmax, color = name)) +
geom_point(aes(size=prcp), alpha = .5) +
geom_smooth(size = 2, se = FALSE)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Warning: Removed 3 rows containing missing values (geom_point).
weather_df %>%
ggplot(aes(x = date, y = tmax, color = name)) +
geom_smooth(size = 2, se = FALSE)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 3 rows containing non-finite values (stat_smooth).
2d density
weather_df %>%
ggplot(aes(x = tmin, y = tmax)) +
geom_bin2d()
## Warning: Removed 15 rows containing non-finite values (stat_bin2d).
Understand the distribution of a single variable
weather_df %>%
ggplot(aes(x = tmax, fill = name)) +
geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 3 rows containing non-finite values (stat_bin).
weather_df %>%
ggplot(aes(x = tmax, fill = name)) +
geom_histogram(position = "dodge")
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 3 rows containing non-finite values (stat_bin).
weather_df %>%
ggplot(aes(x = tmax, fill = name)) +
geom_histogram(position = "dodge") +
facet_grid(~name)
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## Warning: Removed 3 rows containing non-finite values (stat_bin).
weather_df %>%
ggplot(aes(x = tmax, fill = name)) +
geom_density(alpha = .3)
## Warning: Removed 3 rows containing non-finite values (stat_density).
weather_df %>%
ggplot(aes(x = name, y = tmax)) +
geom_boxplot()
## Warning: Removed 3 rows containing non-finite values (stat_boxplot).
weather_df %>%
ggplot(aes(x = name, y = tmax)) +
geom_violin()
## Warning: Removed 3 rows containing non-finite values (stat_ydensity).
Ridge plots!!
weather_df %>%
ggplot(aes(x= tmax, y= name)) +
geom_density_ridges()
## Picking joint bandwidth of 1.84
## Warning: Removed 3 rows containing non-finite values (stat_density_ridges).
##saving a plot
ggp_ridge_temp =
weather_df %>%
ggplot(aes(x = tmax, y = name)) +
geom_density_ridges()
ggsave("ggplot_temp_ridge.pdf", ggp_ridge_temp)
## Saving 7 x 5 in image
## Picking joint bandwidth of 1.84
## Warning: Removed 3 rows containing non-finite values (stat_density_ridges).
weather_df %>%
ggplot(aes(x = tmin, y = tmax, color = name)) +
geom_point(alpha = .4) +
geom_smooth(se = FALSE)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 15 rows containing non-finite values (stat_smooth).
## Warning: Removed 15 rows containing missing values (geom_point).
weather_df %>%
ggplot(aes(x = tmin, y = tmax, color = name)) +
geom_point(alpha = .4) +
geom_smooth(se = FALSE)
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'
## Warning: Removed 15 rows containing non-finite values (stat_smooth).
## Warning: Removed 15 rows containing missing values (geom_point).